Collections > Electronic Theses and Dissertations > Computational Tools for Classifying and Visualizing RNA Structure Change in High-Throughput Experimental Data

Mutations (or Single Nucleotide Variants) in folded RiboNucleic Acid (RNA) structures that cause local or global conformational change are riboSNitches. Predicting riboSNitches is challenging, as it requires making two, albeit related, structure predictions. The data most often used to experimentally validate riboSNitch predictions is Selective 2’ Hydroxyl Acylation by Primer Extension, or SHAPE. Experimentally establishing a riboSNitch requires the quantitative comparison of two SHAPE traces: wild-type (WT) and mutant. Historically, SHAPE data was collected on electropherograms and change in structure was evaluated by “gel gazing.” SHAPE data is now routinely collected with next generation sequencing and/or capillary sequencers. We aim to establish a classifier capable of simulating human “gazing” by identifying features of the SHAPE profile that human experts agree “looks” like a riboSNitch. Additionally, when an RNA molecule folds, it does not always adopt a single, well-defined conformation. The folding energy landscape of the RNA is highly dependent on sequence and the molecular environment. Endogenous molecules, especially in the cellular context, will in some cases completely alter the energy landscape and therefore the ensemble of likely low-energy conformations. The effects of these energy landscape changes on the conformational ensemble are particularly challenging to visualize for larger RNAs including most messenger RNAs (mRNAs). We propose here a robust approach for visualizing the conformational ensemble of RNAs particularly well suited for in vitro vs. in vivo comparisons.